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1. Algorithmic Trading: What Is It and How Does It Work? Algorithmic trading is a type of trading that uses complex algorithms to determine the best time to buy or sell a stock or other financial instrument. Algorithmic trading systems use mathematical models and computer programs to make decisions about when to buy and sell. These systems can scan markets for potential opportunities, monitor news and events, and execute trades according to predetermined rules. Algorithmic trading has become increasingly popular as technology and computing power have advanced, making it possible to execute trades faster and with greater precision. Algorithmic trading can be used to help traders achieve better returns and reduce risk by eliminating emotions from the decision-making process.
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BONE: A Unifying Machine Learning Framework for Methods that Perform Bayesian Online Learning in Non-Stationary Environments - MarkTechPost
In this paper, researchers from Queen Mary University of London, UK, University of Oxford, UK, Memorial University of Newfoundland, Canada, and Google DeepMind Moutain View, CA, USA proposed a unifying framework, BONE (Bayesian Online learning in Non-stationary Environments) for Bayesian online learning in dynamic settings. BONE addresses challenges such as online continual learning, prequential forecasting, and contextual bandits. It requires three modeling components: a model for measurements, an auxiliary process to model non-stationarity and a conditional prior over model parameters. Moreover, two algorithms are also developed to estimate beliefs about model parameters and auxiliary variables, framing existing methods as instances
Unveiling Interpretable Features in Protein Language Models through Sparse Autoencoders - MarkTechPost
Protein language models (PLMs) have significantly advanced protein structure and function prediction by leveraging the vast diversity of naturally evolved protein sequences. However, their internal mechanisms still need to be better understood. Recent interpretability research offers tools to analyze the representations these models learn, which is essential for improving model design and uncovering biological insights. Understanding how PLMs process information can reveal spurious correlations, assess generalizability, and identify new biological principles. This analysis helps refine model biases and learning algorithms, ensuring reliability. Moreover, it sheds light on whether PLMs genuinely capture physical and chemical principles or merely memorize structural patterns.
The Allen Institute for AI (AI2) Releases Tülu 3: A Set of State-of-the-Art Instruct Models with Fully Open Data, Eval Code, and Training Algorithms - MarkTechPost
The Allen Institute for AI (AI2) has announced the release of Tülu 3, a state-of-the-art family of instruction-following models designed to set a new benchmark in AI capabilities. This release includes state-of-the-art features, methodologies, and tools, providing researchers and developers with a comprehensive, open-source solution. With Tülu 3, AI2 has successfully addressed a broad range of tasks, from conversational AI to complex problem-solving domains such as mathematics, reasoning, and evaluation. Tülu 3 is a model family prioritizing transparency, openness, and state-of-the-art performance. The models are based on Meta's Llama 3.1 framework and have been fine-tuned on an extensive dataset mix
algorithm - Strategies for making NPCs respond to player queries based on the NPC's knowledge or activity - Game Development Stack Exchange
I'm developing a finite state machine AI where some interaction with the NPC can be made through a popup window with some choices. The image below shows how it looks: The player (on the right in t...
How Spotify Implemented Personalized Audiobook Recommendations? | by Saankhya Mondal | Nov, 2024 | Towards Data Science
Spotify developed a Graph Neural Network-based recommendation algorithm, named 2T-HGNN (Two Tower - Heterogeneous Graph Neural Network) to personalize audiobook recommendations for its users.